The invention relates to signal processing and, more specifically, to a multi-resolution spatial integration signal processor and method for detecting objects having minimum contrast relative to their background in a digitized two-dimensional image.
Self-guided missiles have demonstrated high effectiveness in operations against surface ships. Both infrared (IR) and radio frequency (RF) means have historically each found application as the sensing element (seeker) in antiship missile (ASM) guidance units. Some well-known advantages of IR technology in this application include passive operation, good resistance to jamming, and high spatial resolution.
The impetus for high spatial resolution follows from potential operational needs for target classification: assuming adequate signal-to-noise ratio (SNR), high classification accuracy requires high spatial resolution, regardless of whether the imagery is interpreted by a man or processed by a computer.
A key limitation of IR ASM seekers is limited range performance under conditions of poor atmospheric visibility. Means previously investigated for maximizing IR sensor SNR, relevant to the ASM seeker application, include waveband optimization, advanced IR detector developments, and multi-frame image processing.
Despite the methods described above, ASM acquisition range is still an important performance index. Improvements in acquisition range (over ranges obtained by hot-spot detection) remains an important objective, in the design of next generation antiship missile seekers.
An early multi-resolution processing application involved the use of quad trees for encoding 1-bit images (see D.H. Ballard and C.M. Brown, Computer Vision, Prentice Hall, Inc., Englewood Cliffs, N.J. (1982)). A variety of additional multi-resolution image processing applications and computational methods are discussed in Multiresolution Image Processing and Analysis, A. Rosenfeld, ed., Springer-Verlag, Berlin, 1984, and L. O'Gorman and A.C. Sanderson, "A Comparison of Methods and Computation for Multi-Resolution Low- and Band-Pass Transforms for Image Processing," Computer Vision, Graphics, and Image Processing, Vol. 37, pp. 386-401 (1987).
Obviously, the idea of performing simultaneous analysis of images at multiple spatial resolutions is not new. However, previous algorithms and special purpose computers designed for multi-resolution processing are organized around the idea of an image "pyramid", with the original image at the pyramid's base, and successively reduced resolution images at successively higher levels in the pyramid.
One aspect of an image pyramid is that the progressively reduced resolution images have progressively reduced dimensionality, i.e., a progressively smaller number of samples per image, giving rise to a tapering of the pyramid at its top to a single picture element. The earlier multi-resolution approaches generally degrade resolution simultaneously in both dimensions: moving up one level in the pyramid implies reduced resolution in both image coordinates. The previous multi-resolution techniques retain full pixel-level information for images at all resolutions. Consequently, estimates of memory requirements include allowing for full-frame memory at the original, highest-detail, resolution. Finally, the earlier approaches are not designed to exploit the characteristics of any particular image-forming process. Thus, memory requirements and execution speed are independent of whether the image is acquired one column at a time, via a single pixel raster, etc.: in all cases a full frame must be acquired and buffered before the data can be processed.